arXiv subjects

Jean Provost

Publications and source records attributed to Jean Provost.

At least 19 recordsLinked to original sources

arXiv2026

ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging

Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public datasets remain sparse, restricting rigorous evaluation and slowing progress in algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce \textbf{ULMShare}, an open-access dataset of 99 whole-brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation, Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full dataset is available on the Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.

physics.med-ph↗

arXiv2026

Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates

Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP) brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic quantification. Extending this approach to 3D transcranial ULM imaging in NHPs, we show efficient correction of skull induced aberrations and removal of artifacts, such as vessel duplications. By providing a fully automated and generalizable solution for aberration correction, this work lowers a major technical barrier to transcranial ultrasound imaging, enabling broader adoption of non-invasive, super-resolution and functional neuroimaging across laboratories and across species.

physics.med-ph↗

arXiv2025

The-Bodega: A Matlab Toolbox for Biologically Dynamic Microbubble Simulations on Realistic Hemodynamic Microvascular Graphs

The-Bodega is a Matlab-based toolbox for simulating ground-truth datasets for Ultrasound Localization Microscopy (ULM)-a super resolution imaging technique that resolves microvessels by systematically tracking microbubbles flowing through the microvasculature. The-Bodega enables open-source simulation of stochastic microbubble dynamics through anatomically complex vascular graphs and features a quasi-automated pipeline for generating ground-truth ultrasound data from simple vascular inputs. It incorporates sequential Monte Carlo simulations augmented with Poiseuille flow distributions and dynamic pulsatile flow. A key novelty of our framework is its flexibility to accommodate arbitrary vascular architectures and benchmark common ULM algorithms, such as Fourier Ring Correlation and Singular Value Decomposition (SVD) spatiotemporal filtering, on realistic hemodynamic digital phantoms. The-Bodega supports consistent microbubble-to-ultrasound simulations across domains ranging from mouse brains to human hearts and automatically leverages available CPU/GPU parallelization to improve computational efficiency. We demonstrate its versatility in applications including image quality assessment, motion artifact analysis, and the simulation of novel ULM modalities, such as capillary imaging, myocardial reconstruction under beating heart motion, and simulating neurovascular evoked responses.

physics.med-ph↗

arXiv2024

Functional Assessment of Cerebral Capillaries using Single Capillary Reporters in Ultrasound Localization Microscopy

The brain's microvascular cerebral capillary network plays a vital role in maintaining neuronal health, yet capillary dynamics are still not well understood due to limitations in existing imaging techniques. Here, we present Single Capillary Reporters (SCaRe) for transcranial Ultrasound Localization Microscopy (ULM), a novel approach enabling non-invasive, whole-brain mapping of single capillaries and estimates of their transit-time as a neurovascular biomarker. We accomplish this first through computational Monte Carlo and ultrasound simulations of microbubbles flowing through a fully-connected capillary network. We unveil distinct capillary flow behaviors which informs methodological changes to ULM acquisitions to better capture capillaries in vivo. Subsequently, applying SCaRe-ULM in vivo, we achieve unprecedented visualization of single capillary tracks across brain regions, analysis of layer-specific capillary heterogeneous transit times (CHT), and characterization of whole microbubble trajectories from arterioles to venules. Lastly, we evaluate capillary biomarkers using injected lipopolysaccharide to induce systemic neuroinflammation and track the increase in SCaRe-ULM CHT, demonstrating the capability to detect subtle capillary functional changes. SCaRe-ULM represents a significant advance in studying microvascular dynamics, offering novel avenues for investigating capillary patterns in neurological disorders and potential diagnostic applications.

physics.med-ph↗

arXiv2024

3D transcranial Dynamic Ultrasound Localization Microscopy in the mouse brain using a Row-Column Array

The role of brain hemodynamics in neurodegenerative diseases cannot be fully assessed using existing imaging technologies. Recently, 2D Dynamic Ultrasound Localization Microscopy (DULM) has allowed for the quantitative mapping of the pulsatile flow at sub-wavelength resolution. However, to obtain accurate velocity estimates, 3D imaging is more adapted, especially for complex vascularized organs like the brain. 3D+t DULM is achievable using matrix array probes, but suffers from limitations in terms of cost, device complexity associated with the high channel count, and operating frequencies. Alternatively, Row Column Arrays (RCA) can reduce the number of elements while maintaining a large field of view and high frame rate. Herein, we demonstrate the feasibility of performing 3D+t blood flow measurements in the mouse brain using an RCA and a DULM sequence with a high spatiotemporal resolution. Transcranial images of anesthetized mice (n=7) were acquired at a volume rate of 750 Hz using 42 tilted plane waves. After microbubbles localization and tracking, super-resolved dynamic density and velocity maps of the 3D brain vascular network were obtained. Cortical vessels were segmented and pulsatility in the arteries was significantly higher than in veins for all mice, in accordance with the literature. Our results demonstrate the feasibility and reproducibility of achieving high spatiotemporal resolution volumes of the mouse brain vasculature with DULM using a RCA.

physics.med-ph↗

arXiv2024

Towards Transcranial 3D Ultrasound Localization Microscopy of the Nonhuman Primate Brain

Hemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualization and quantification of cerebral blood flow would help better understand the underlying mechanism of those cerebrovascular diseases. 3D ultrasound localization microscopy (ULM) is a novel technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. Here, we demonstrated the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channels programmable ultrasound scanner. We achieved a highly resolved vascular map of the macaque brain at large depth in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 {\mu}m. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60.4 {\mu}m. This work paves the way to clinical application of 3D ULM.

eess.IV↗

arXiv2024

Boosting Cardiac Color Doppler Frame Rates with Deep Learning

Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ventricular filling. Concurrently, deep learning is demonstrating promising outcomes in post-processing of echocardiographic data for various applications. This work explores the use of deep learning models for intracardiac Doppler velocity estimation from a reduced number of filtered I/Q signals. We used a supervised learning approach by simulating patient-based cardiac color Doppler acquisitions and proposed data augmentation strategies to enlarge the training dataset. We implemented architectures based on convolutional neural networks. In particular, we focused on comparing the U-Net model and the recent ConvNeXt models, alongside assessing real-valued versus complex-valued representations. We found that both models outperformed the state-of-the-art autocorrelator method, effectively mitigating aliasing and noise. We did not observe significant differences between the use of real and complex data. Finally, we validated the models on in vitro and in vivo experiments. All models produced quantitatively comparable results to the baseline and were more robust to noise. ConvNeXt emerged as the sole model to achieve high-quality results on in vivo aliased samples. These results demonstrate the interest of supervised deep learning methods for Doppler velocity estimation from a reduced number of acquisitions.

eess.IV↗

arXiv2024

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual microbubbles injected in the bloodstream. Mapping the whole angioarchitecture requires the accumulation of microbubbles trajectories from thousands of frames, typically acquired over a few minutes. ULM acquisition times can be reduced by increasing the microbubble concentration, but requires more advanced algorithms to detect them individually. Several deep learning approaches have been proposed for this task, but they remain limited to 2D imaging, in part due to the associated large memory requirements. Herein, we propose to use sparse tensor neural networks to reduce memory usage in 2D and to improve the scaling of the memory requirement for the extension of deep learning architecture to 3D. We study several approaches to efficiently convert ultrasound data into a sparse format and study the impact of the associated loss of information. When applied in 2D, the sparse formulation reduces the memory requirements by a factor 2 at the cost of a small reduction of performance when compared against dense networks. In 3D, the proposed approach reduces memory requirements by two order of magnitude while largely outperforming conventional ULM in high concentration settings. We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i.e. the use of higher concentration in silico and reduced acquisition time.

eess.IV↗

arXiv2024

Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data

Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. We propose to use the \textit{a priori} knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC \textit{in vivo} in 5 mouse brains. We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $\mu$m to 18.3 $\mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction.

eess.IV↗

arXiv2023

A unified framework combining coherent compounding, harmonic imaging and angular coherence for simultaneous high-quality B-mode and tissue Doppler in ultrafast echocardiography

Various methods have been proposed to enhance image quality in ultrafast ultrasound. Coherent compounding can improve image quality using multiple steered diverging transmits when motion occurring between transmits is corrected for. Harmonic imaging, a standard technique in conventional focused echocardiography, has been adapted for ultrafast imaging, reducing clutter. Coherence-based approaches have also been shown to increase contrast in clinical settings by enhancing signals from coherent echoes and reducing clutter. Herein, we introduce a simple, unified framework that combines motion-correction, harmonic imaging, and angular-coherence, showing for the first time that their benefits can be combined in real-time. Validation was conducted through in vitro testing on a spinning disk model and in vivo on 4 volunteers. In vitro results confirmed the unified framework capability to achieve high contrast in large-motion contexts up to 17 cm/s. In vivo testing highlighted proficiency in generating images of high quality during low and high tissue velocity phases of the cardiac cycle. Specifically, during ventricular filling, the unified framework increased the gCNR from 0.47 to 0.87 when compared against coherent compounding.

physics.bio-ph↗

arXiv2023

Dynamic Imaging using any Ultrasound Localization Microscopy Dataset

Ultrasound Localization Microscopy (ULM) relies on the injection of microbubbles (MBs) to obtain highly resolved density maps of blood circulation in vivo, with a resolution that can reach 10 {\mu}m ~ {\lambda}/10 in the rodent brain. Static mean velocity maps can be extracted but are intrinsically biased by potential significant changes in the number of MBs detected during the cardiac cycle. Dynamic ULM (DULM) is a technique developed for non-invasive pulsatility measurements in the brain of rodents, leading to temporally resolved velocity and density cine-loops. It was previously based on external triggers such as the electrocardiogram (ECG), limiting its use to datasets acquired specifically for DULM applications while also increasing the required acquisition time. This study presents a new motion matching method using tissue Doppler that eliminates the need for ECG-gating in DULM experiments. DULM can now be performed on any ULM datasets, recovering pertinent temporal information, and improving the robustness of the mean velocity estimates.

physics.med-ph↗

arXiv2023

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM)

Ultrasound Localization Microscopy (ULM) has recently enabled the mapping of the cerebral vasculature in vivo with a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20.000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conducted in silico using the SIMUS simulation software in an anatomically realistic phantom and then compared to in vivo acquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6 um and 9.93 um for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.

physics.med-ph↗

arXiv2023

Equivalent Time Active Cavitation Imaging

The development of neurologically active drugs faces a challenge in treating brain diseases due to the blood-brain barrier's impermeability. High-intensity focused ultrasound can open the barrier in a targeted manner, expanding treatment options. However, this procedure risks brain damage and off-target effects without proper monitoring. Current monitoring methods have limitations. We present Equivalent Time Active Cavitation Imaging, a novel ultrasound modality that characterizes the ultrasound pressure field during treatment. This system utilizes a unique plane wave sequence and an algorithm to filter the microbubble signal for pressure field mapping. We conducted pressure measurements in vitro for comparison with hydrophones and in vivo during blood-brain barrier opening in mice. Our modality successfully spatiotemporally characterizes the pressure field with an active imaging setup, demonstrating good correlation with hydrophone measurements. Experimental in vivo results are promising, establishing the feasibility of this monitoring method. In conclusion, this proof of concept represents a significant step toward a new ultrasound modality for monitoring blood-brain barrier opening using focused ultrasound. This approach offers a safer and more precise monitoring method, opening new possibilities for treating brain diseases.

physics.med-ph↗

arXiv2023

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy

Ultrasound Localization Microscopy can resolve the microvascular bed down to a few micrometers. To achieve such performance microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which lead to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo on a rat brain acquisition. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 $\mu$m with an increase in resolution when compared against a conventional approach.

cs.CV↗

arXiv2023

Ergodic encoding for single-element ultrasound imaging in vivo

Conventional ultrasound imaging relies on the computation of geometric time delay from multiple sensors to detect the position of a scatterer. In this paper, we present Ergodic Relay Ultrasound Imaging (ERUI), a method that utilizes an ergodic cavity down to a single ultrasonic sensor for ultrasound imaging. With the proposed method, the ergodic cavity creates a unique temporal signature that encodes the position of a scatterer. When compared to standard approaches, ERUI enables the generation of images of comparable quality while utilizing fewer detector elements. Our results suggest that ERUI has the potential to achieve image resolution similar to that of traditional imaging techniques, shifting the complexity from hardware to sofware. The demonstrated feasibility offers a promising path towards ultrasound probes with reduced costs and complexity for more portable scanning devices.

physics.med-ph↗

arXiv2023

Equivalent-Time-Active-Cavitation-Imaging Enables Vascular-Resolution Blood-Brain-Barrier-Opening-Therapy Planning

Linking cavitation and anatomy was found to be important for predictable outcomes in Focused-Ultrasound Blood-Brain-Barrier-Opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either 1) do not leverage the full resolution capabilities of ultrasound imaging or 2) place strong constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Herein, we develop BP-ETACI, derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood-brain barrier opening with long therapeutic bursts using a 1.5-MHz focused transducer in the brain of C57BL/6 mice. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for 1) monitoring FUS-BBBO, but also for 2) therapy planning and target verification. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

physics.med-ph↗

arXiv2023

A Tracking prior to Localization workflow for Ultrasound Localization Microscopy

Ultrasound Localization Microscopy (ULM) has proven effective in resolving microvascular structures and local mean velocities at sub-diffraction-limited scales, offering high-resolution imaging capabilities. Dynamic ULM (DULM) enables the creation of angiography or velocity movies throughout cardiac cycles. Currently, these techniques rely on a Localization-and-Tracking (LAT) workflow consisting in detecting microbubbles (MB) in the frames before pairing them to generate tracks. While conventional LAT methods perform well at low concentrations, they suffer from longer acquisition times and degraded localization and tracking accuracy at higher concentrations, leading to biased angiogram reconstruction and velocity estimation. In this study, we propose a novel approach to address these challenges by reversing the current workflow. The proposed method, Tracking-and-Localization (TAL), relies on first tracking the MB and then performing localization. Through comprehensive benchmarking using both in silico and in vivo experiments and employing various metrics to quantify ULM angiography and velocity maps, we demonstrate that the TAL method consistently outperforms the reference LAT workflow. Moreover, when applied to DULM, TAL successfully extracts velocity variations along the cardiac cycle with improved repeatability. The findings of this work highlight the effectiveness of the TAL approach in overcoming the limitations of conventional LAT methods, providing enhanced ULM angiography and velocity imaging.

physics.med-ph↗

arXiv2023

Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this paper, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e. high quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on non-simulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

eess.IV↗